Road sign recognition in digital image data is typically based on a two-step approach. The first step is determining geometric shapes mapped in the image data, such as circles, rectangles or triangles which could represent road signs. The respective regions in the image data are then typically examined for certain further features using a pictogram classifier, in order to determine an exact type of road sign.
A generic method of this kind is disclosed in the DE 10 2009 048 066 A1. Here a method for road sign recognition is described which analyses and classifies the image data of a sensor in an information processing unit. A first method step, on a basis of the results of an analysis, at least one image section is determined which is very likely to contain an object which is a road sign belonging to a certain class of road sign, and which image section, in a second method step, is sent to a classifier, which on the basis of the selected image section recognizes a road sign of a certain class by means of a learning-based method. A first method step involves identifying a class-specific characteristic in the image section, generating a modified image section having the class-specific characteristic in the image center thereof. The image areas created by moving the class-specific characteristic into the center of the image are filled with suitable pixels, and feeding the modified image section into the classifier.
The problem with the known method is the so-called “false-positive-rate”. This indicates the rate at which traffic signs are recognized in the image data, when in reality there are none. For circular traffic signs the known methods are largely sufficient for achieving a good classification rate for a simultaneously low false-positive rate. By contrast, the false-positive rate for rectangular structures mapped in the image data is markedly higher. The main reason for this is that rectangular structures which are not traffic signs are encountered much more frequently in urban environments than round structures. Such structures are typically advertising posters and billboards, menu advertisements etc.
It is therefore at least one object to achieve road sign recognition at a lower false-positive rate. In addition, other objects, desirable features and characteristics will become apparent from the subsequent summary and detailed description, and the appended claims, taken in conjunction with the accompanying drawings and this background.